{"title":"基于CNN的混合噪声分类与抑制","authors":"R. Baardman","doi":"10.3997/2214-4609.201803017","DOIUrl":null,"url":null,"abstract":"In this abstract a novel machine learning deblending algorithm is introduced. The method uses a convolutional neural netork (CNN) to classify data patches in a \"blended\" and a \"non-blended\" class. A second, regression based, CNN deblends the \"blended\" patches. Results are shown for a synthetic data example.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"270 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Classification And Suppression Of Blending Noise Using CNN\",\"authors\":\"R. Baardman\",\"doi\":\"10.3997/2214-4609.201803017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this abstract a novel machine learning deblending algorithm is introduced. The method uses a convolutional neural netork (CNN) to classify data patches in a \\\"blended\\\" and a \\\"non-blended\\\" class. A second, regression based, CNN deblends the \\\"blended\\\" patches. Results are shown for a synthetic data example.\",\"PeriodicalId\":231338,\"journal\":{\"name\":\"First EAGE/PESGB Workshop Machine Learning\",\"volume\":\"270 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First EAGE/PESGB Workshop Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.201803017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First EAGE/PESGB Workshop Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201803017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification And Suppression Of Blending Noise Using CNN
In this abstract a novel machine learning deblending algorithm is introduced. The method uses a convolutional neural netork (CNN) to classify data patches in a "blended" and a "non-blended" class. A second, regression based, CNN deblends the "blended" patches. Results are shown for a synthetic data example.
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